We also evaluate BCNet on two amodal instance segmentation benchmarks:
(1) KINS [1]}, built on the original KITTI [2]}, is the largest amodal segmentation benchmark for traffic scenes with both annotated amodal and modal masks for instances. BCNet is trained on the training split (7,474 images and 95,311 instances) and tested on the testing split (7,517 images and 92,492 instances) following the setting in [1]}.
(2) COCOA [4]} is a subpart of COCO [5]}, where we train BCNet on the official training split (2,500 images) and test on the validation split (1,323 images). Note that each instance has no class label and we only use the modal and amodal mask labels for the COCOA dataset.
